Use of Sea Surface Temperature for Cyclone Intensity Prediction Needs a Relook

Ali, M. M.; Kashyap, Tina; Nagamani, P. V.

doi:10.1002/2013eo190005

Cited by 23 publications

(13 citation statements)

References 10 publications

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“…Intense upper ocean mixing events caused by the strong hurricane winds can quickly erode the thermal signature of subsurface warm or cold features (Pickard and Emery, 1990), leading to SST misrepresenting the ocean thermal energy, and potential TC intensity. Several studies (e.g., Mao et al, 2000;Shay et al, 2000;Ali et al, 2007Ali et al, , 2013Mainelli et al, 2008;Lin et al, 2013b) demonstrated the importance of ocean thermal energy, represented by warm ocean features.…”

Section: Tropical Cyclonesmentioning

confidence: 99%

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

et al. 2019

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Over the past decade, measurements from the climate-oriented ocean observing system have been key to advancing the understanding of extreme weather events that originate and intensify over the ocean, such as tropical cyclones (TCs) and extratropical bomb cyclones (ECs). In order to foster further advancements to predict and better understand these extreme weather events, a need for a dedicated observing system component specifically to support studies and forecasts of TCs and ECs has been identified, but such a system has not yet been implemented. New technologies, pilot networks, targeted deployments of instruments, and state-of-the art coupled numerical models have enabled advances in research and forecast capabilities and illustrate a potential framework for future development. Here, applications and key results made possible by the different ocean observing efforts in support of studies and forecasts of TCs and ECs, as well as recent advances in observing technologies and strategies are reviewed. Then a vision and specific recommendations for the next decade are discussed.

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Section: Tropical Cyclonesmentioning

confidence: 99%

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

et al. 2019

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“…Therefore, cooling of SST due to strong cyclonic winds of the same day is ruled out. Since the number of cyclone with a negative correlation between CI and SST are more than those having positive correlations, Ali et al [17] suggested to have a relook at the use of satellite derived SST as a measure of CI.…”

Section: Ssha Sst and CImentioning

confidence: 99%

Importance of Ocean Heat Content for Cyclone Studies

Modave¹,

Shokar²

2014

J Oceanogr Mar Res

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The physical process involved from formation to dissipation of any cyclone is very complex in nature. Thus, accurate and timely prediction of cyclone intensity and track still remain as challenging scientific problems. Though presently available cyclone prediction models have improved our understanding about many physical processes from cyclone formation to dissipation, some are still unresolved. The input parameters that used into the models as a source of energy for the cyclones need to be reconsidered. Model estimate the energy for cyclones as latent heat release through evaporation computed using only Sea Surface Temperature (SST). However, the energy in the ocean for cyclone is available in the upper layers of the oceans rather than in the skin layer represented by SST. Therefore we examined the importance of heat content of the upper ocean in estimating four important aspects of cyclones (i) pressure drop, (ii) track change, (iii) intensity and (iv) storm surge height. The results obtained during the series of analysis emphasized that the thermal structure of the upper ocean is more critical and sensitive predictor for cyclones compared to SST, which need to be incorporated in numerical models for better and accurate forecasting.

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“…MLP approach has been used by Ali et al . (, , , , ) to estimate the tropical cyclone heat potential and the ocean windstress. We tried all the models and found that an MLP network provided the least error and best results.…”

Section: Approachmentioning

confidence: 99%

“…( , 2012cAli et al . ( , 2013aAli et al . ( , 2013b to estimate the tropical cyclone heat potential and the ocean windstress.…”

Section: Ann Approachmentioning

confidence: 99%

A soft‐computing cyclone intensity prediction scheme for the Western North Pacific Ocean

Sharma

Ali

Knaff

et al. 2013

Atmospheric Science Letters

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A soft-computing cyclone intensity prediction scheme (SCIPS) is introduced using an artificial neural network (ANN) approach and adding ocean heat content, as an additional predictor to the normally used atmospheric parameters, to predict tropical cyclone intensity change in the western north Pacific Ocean. We used 1997-2004 data to develop and validate this scheme. The ANN-based estimations have been compared with observations and estimations using the multiple linear regression (MLR). SCIPS performance improves upon MLR as the lead hour increases from 12 to 120 h and also for high intensifying cyclones.

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Use of Sea Surface Temperature for Cyclone Intensity Prediction Needs a Relook

Cited by 23 publications

References 10 publications

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

Importance of Ocean Heat Content for Cyclone Studies

A soft‐computing cyclone intensity prediction scheme for the Western North Pacific Ocean

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